The spectacular success ofdeep generativemodels calls forquantitativetools to measure their statistical performance. Divergence frontiers have recently been proposed as an evaluation framework for generative models, due to their ability to measure the quality-diversity trade-off inherent to deep generative modeling. We establish non-asymptotic bounds on the sample complexity of divergence frontiers.

The use of machine learning models in high-stake applications (e.g., healthcare, lending, college admission) has raised growing concerns due to potential biases

Do the main claims made in the abstract and introduction accurately reflect the paper's

Augmentations Graph Edit Operators Node DroppingNode Deletion Edge PerturbationEdge Deletion, Edge Addition Categorical Attribute MaskingFeature Masking Operator Sub-graph SamplingNode Deletions Forexample, consideragraphg A( |g), generated vianodedropping.